WO2022054278A1 - 推論装置および学習装置 - Google Patents

推論装置および学習装置 Download PDF

Info

Publication number
WO2022054278A1
WO2022054278A1 PCT/JP2020/034709 JP2020034709W WO2022054278A1 WO 2022054278 A1 WO2022054278 A1 WO 2022054278A1 JP 2020034709 W JP2020034709 W JP 2020034709W WO 2022054278 A1 WO2022054278 A1 WO 2022054278A1
Authority
WO
WIPO (PCT)
Prior art keywords
defrosting
temperature information
time
temperature
frost melting
Prior art date
Application number
PCT/JP2020/034709
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
惇 川島
玄太 吉村
卓爾 森本
啓介 杉浦
Original Assignee
三菱電機株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 三菱電機株式会社 filed Critical 三菱電機株式会社
Priority to CN202080104245.2A priority Critical patent/CN115997089A/zh
Priority to DE112020007604.0T priority patent/DE112020007604T5/de
Priority to PCT/JP2020/034709 priority patent/WO2022054278A1/ja
Priority to JP2022547361A priority patent/JPWO2022054278A5/ja
Publication of WO2022054278A1 publication Critical patent/WO2022054278A1/ja

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/41—Defrosting; Preventing freezing
    • F24F11/42—Defrosting; Preventing freezing of outdoor units
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data

Definitions

  • the present disclosure relates to an inference device and a learning device for an air conditioner having a defrosting function.
  • Patent Document 1 when a certain period of time has elapsed since the previous defrosting operation was completed and the heating operation was restarted, and the temperature detected by the temperature sensor became equal to or lower than the specified value, the defrosting operation was started. Assuming that the conditions are met, the defrosting operation is started.
  • the defrosting operation may be started even though the outdoor heat exchanger has almost no frost, which is inferior in energy saving and comfort. was there.
  • This disclosure is made in order to solve such a problem, and obtain an inference device and a learning device capable of improving energy saving by appropriately determining the start time of the defrosting operation. It is an object.
  • the inference device is an inference device for obtaining an inferred value of the time required for frost melting with respect to the defrosting temperature information indicating the temperature of the outdoor heat exchanger of the outdoor unit of the air conditioner or the state of change in the temperature.
  • the time required for frost melting is a period during which the temperature of the outdoor heat exchanger stabilizes within the first range, and the inference device acquires the defrost temperature information of the air conditioner.
  • the above-mentioned with respect to the defrosting temperature information based on the defrosting temperature information acquired by the first data acquisition unit using a data acquisition unit and a learned model that infers the defrosting time required from the defrosting temperature information. It is equipped with an inference unit for obtaining an inferred value of the time required for frost melting.
  • the learning device is a trained model for obtaining an inferred value of the frost melting time for the defrosting temperature information indicating the temperature of the outdoor heat exchanger of the outdoor unit of the air conditioner or the state of the change in the temperature.
  • the required time for frost melting is a period in which the temperature of the outdoor heat exchanger is stable within the first range
  • the learning device is the defrosting temperature information and the frost melting.
  • the second data acquisition unit that acquires the training data created based on the combination with the measured value of the required time and the learning data
  • the inferred value of the frost melting required time for the defrosting temperature information can be obtained.
  • a model generation unit that generates a trained model for obtaining an inferred value of the frost melting time from the defrosting temperature information of the air conditioner by learning so as to approach the measured value of the frost melting time. It is prepared.
  • the inference device and the learning device by inferring the time required for frost melting as the amount of frost formation in the outdoor heat exchanger, the start time of the defrosting operation is appropriately determined, and energy saving is improved. Can be planned.
  • FIG. 1 It is a schematic diagram which shows an example of the structure of the refrigerant circuit 100 of the air conditioner 101 to which the inference device 20 and the learning device 30 which concerns on Embodiment 1 are applied. It is a flowchart which shows an example of the defrosting operation control of the air conditioner 101 which concerns on Embodiment 1. FIG. It is a figure which shows an example of the time-dependent change of the defrosting temperature ⁇ during the defrosting operation. It is a block diagram which shows the structure of the inference apparatus 20 which concerns on Embodiment 1. FIG. It is a flowchart which shows the process flow of the inference apparatus 20 which concerns on Embodiment 1. FIG.
  • FIG. 1 It is a flowchart which shows the flow of the process of the air conditioner 101 which concerns on Embodiment 1.
  • FIG. It is a block diagram which shows the structure of the learning apparatus 30 which concerns on Embodiment 1.
  • FIG. It is a figure which showed typically the example of the model of the neural network 34 provided in the model generation part 32. It is a flowchart which shows the process flow of the learning apparatus 30 which concerns on Embodiment 1.
  • the present disclosure is not limited to the following embodiments, and can be variously modified without departing from the gist of the present disclosure.
  • the present disclosure includes all combinations of configurations that can be combined among the configurations shown in the following embodiments and modifications thereof. Further, in each figure, those having the same reference numerals are the same or equivalent thereof, which are common to the whole text of the specification. In each drawing, the relative dimensional relationship or shape of each component may differ from the actual one.
  • Embodiment 1 the inference device 20 and the learning device 30 according to the first embodiment will be described.
  • the inference device 20 and the learning device 30 are mounted or connected to the air conditioner 101 for use.
  • FIG. 1 is a schematic diagram showing an example of the configuration of the refrigerant circuit 100 of the air conditioner 101 to which the inference device 20 and the learning device 30 according to the first embodiment are applied.
  • the air conditioner 101 includes an indoor unit 1 arranged in an indoor space to be air-conditioned and an outdoor unit 2 arranged outside the room.
  • the environment in which the outdoor unit 2 is installed is hereinafter referred to as an outside air environment.
  • the indoor unit 1 includes an indoor heat exchanger 5.
  • the outdoor unit 2 includes a compressor 3, a four-way valve 4, an outdoor heat exchanger 6, and an expansion valve 7.
  • the compressor 3, the four-way valve 4, the outdoor heat exchanger 6, the expansion valve 7, and the indoor heat exchanger 5 are connected via a refrigerant pipe to form a refrigerant circuit 100.
  • the indoor heat exchanger 5 exchanges heat between the refrigerant flowing through the refrigerant pipes arranged inside and the indoor air.
  • the outdoor heat exchanger 6 exchanges heat between the refrigerant flowing through the refrigerant pipes arranged inside and the outside air.
  • the indoor heat exchanger 5 and the outdoor heat exchanger 6 are, for example, fin-and-tube heat exchangers.
  • the indoor heat exchanger 5 and the outdoor heat exchanger 6 may be divided into a plurality of heat exchangers, respectively. In that case, the plurality of heat exchangers are connected in series or in parallel.
  • the compressor 3 sucks in the refrigerant flowing through the refrigerant circuit 100.
  • the compressor 3 compresses and discharges the sucked refrigerant.
  • the compressor 3 is, for example, an inverter compressor.
  • the refrigerant discharged from the compressor 3 flows into the indoor heat exchanger 5 or the outdoor heat exchanger 6.
  • the four-way valve 4 is a flow path switching device configured to switch the state between a cooling operation for cooling the indoor space in which the indoor unit 1 is arranged and a heating operation for heating.
  • FIG. 1 shows a state in which the air conditioner 101 is performing a heating operation.
  • the four-way valve 4 is in the state shown by the solid line in FIG. 1, and the refrigerant discharged from the compressor 3 exchanges heat on the indoor side. It flows into the vessel 5.
  • the outdoor heat exchanger 6 acts as an evaporator
  • the indoor heat exchanger 5 acts as a condenser.
  • the four-way valve 4 is in the state shown by the broken line in FIG. 1, and the refrigerant discharged from the compressor 3 flows into the outdoor heat exchanger 6. .. At this time, the outdoor heat exchanger 6 acts as a condenser, and the indoor heat exchanger 5 acts as an evaporator. Instead of the four-way valve 4, another flow path switching device having the same function may be used.
  • the expansion valve 7 is a pressure reducing device for reducing the pressure of the refrigerant, and is composed of, for example, an electronic expansion valve.
  • the expansion valve 7 is provided between the outdoor heat exchanger 6 and the indoor heat exchanger 5.
  • another decompression device having the same function may be used.
  • the refrigerant circuit 100 is filled with a refrigerant.
  • the type of the refrigerant is R32, R410A, or the like, and is not particularly limited.
  • the indoor unit 1 further includes an indoor side blower 8 for ventilating the indoor side heat exchanger 5.
  • the indoor side blower 8 is arranged on the windward side of the indoor side heat exchanger 5.
  • the indoor blower 8 may be arranged on the leeward side of the indoor heat exchanger 5.
  • the outdoor unit 2 further includes an outdoor blower 9 for ventilating the outdoor heat exchanger 6.
  • the outdoor blower 9 is arranged on the leeward side of the outdoor heat exchanger 6.
  • the outdoor blower 9 may be arranged on the windward side of the outdoor heat exchanger 6.
  • the outdoor blower 9 is provided with a device that detects or estimates the current value used by the outdoor blower 9 for blowing.
  • the device is referred to as a current measuring device 16.
  • the current measuring device 16 is composed of, for example, a current sensor or a processor.
  • the current information detected or estimated by the current measuring device 16 is output to the control unit 15 provided in the outdoor unit 2.
  • the current measuring device 16 may be provided in the control unit 15.
  • a temperature sensor 10 is attached to the outer shell of the compressor 3 of the outdoor unit 2.
  • the temperature sensor 10 detects the temperature of the compressor 3.
  • the mounting position of the temperature sensor 10 may be any other portion as long as it can detect the temperature of the compressor 3.
  • a temperature sensor 10 may be provided in the refrigerant pipe of the path from the compressor 3 to the four-way valve 4.
  • the compressor temperature information detected by the temperature sensor 10 is output to the control unit 15.
  • a temperature sensor 11 is attached to the windward side of the indoor blower 8 of the indoor unit 1.
  • the temperature sensor 11 detects the air temperature before flowing into the indoor heat exchanger 5, that is, the room temperature.
  • the position of the temperature sensor 11 is not limited to the portion shown in FIG. 1 as long as the room temperature can be detected.
  • the room temperature information detected by the temperature sensor 11 is output to the control unit 15.
  • a temperature sensor 12 is attached to the pipe wall of the refrigerant pipe of the indoor heat exchanger 5.
  • the temperature sensor 12 detects the temperature of the indoor heat exchanger 5 when the indoor heat exchanger 5 functions as a condenser during heating, that is, the condensation temperature.
  • the position of the temperature sensor 12 is not limited to the portion shown in FIG. 1 as long as it can detect the temperature of the indoor heat exchanger 5.
  • the condensation temperature information detected by the temperature sensor 12 is output to the control unit 15.
  • the outdoor unit 2 is equipped with a temperature sensor 13 for measuring the temperature of the air passing through the outdoor heat exchanger 6 by the outdoor blower 9.
  • the temperature sensor 13 is attached to the wind side of the outdoor heat exchanger 6 in order to measure the air temperature before passing through the outdoor heat exchanger 6, that is, the outside air temperature.
  • the position of the temperature sensor 13 is not limited to the portion shown in FIG. 1 as long as it can detect the air temperature before passing through the outdoor heat exchanger 6.
  • the outside air temperature information detected by the temperature sensor 13 is output to the control unit 15.
  • a temperature sensor 14 is attached to the pipe wall of the refrigerant pipe of the outdoor heat exchanger 6.
  • the temperature sensor 14 detects the temperature of the outdoor heat exchanger 6, that is, the evaporation temperature when the outdoor heat exchanger 6 functions as an evaporator during heating.
  • the position of the temperature sensor 14 is not limited to the portion shown in FIG. 1 as long as the temperature of the outdoor heat exchanger 6 can be estimated.
  • the evaporation temperature information detected by the temperature sensor 14 is output to the control unit 15.
  • five temperature sensors 10 to 14 are provided, but the number of temperature sensors 10 to 14 is not limited to the number in FIG. 1, and may be larger or smaller than these.
  • a plurality of temperature sensors 14 may be attached to the outdoor heat exchanger.
  • the type of sensor is not limited to the type shown in FIG.
  • a humidity sensor 17 for measuring the humidity of the outside air environment in which the outdoor unit 2 is installed an illuminance sensor 18 for measuring the illuminance of the outside air environment, or the like may be installed in the outdoor unit 2.
  • both the temperature information and the humidity information of the outside air environment can be obtained by the temperature sensor 13 and the humidity sensor 17.
  • the illuminance measured by the illuminance sensor 18 is preferably a value indicating the amount of solar radiation to the housing of the outdoor unit 6.
  • the information detected by these sensors 10 to 14 and 16 to 18 is collected in the control unit 15 provided in the outdoor unit 2.
  • the control unit 15 is composed of a control board.
  • a control device, a storage device, and a drive circuit are mounted on the control board of the control unit 15.
  • the control device is composed of, for example, dedicated hardware, a CPU (Central Processing Unit central processing unit) or a microprocessor that executes a program stored in a memory.
  • the storage device is a non-volatile or volatile semiconductor memory such as RAM (RandomAccessMemory), ROM (ReadOnlyMemory), flash memory, EPROM (ErasableProgrammableROM), magnetic disk, flexible disk, optical disk, etc. It is a disk of.
  • the refrigerant is sealed in the refrigerant circuit 100, and the refrigerant is compressed by the compressor 3.
  • a refrigeration cycle consisting of the following cooling operation circuits is configured. That is, the refrigerant compressed by the compressor 3 is condensed and liquefied by the outdoor heat exchanger 6, expanded by the expansion valve 7, and further evaporated by the indoor heat exchanger 5 and returned to the compressor 3. .. Also in the defrosting operation, a refrigerating cycle including a cooling operation circuit is configured.
  • a refrigeration cycle consisting of the following heating operation circuits is configured. That is, the refrigerant compressed by the compressor 3 is condensed and liquefied by the indoor heat exchanger 5, expanded by the expansion valve 7, evaporated by the outdoor heat exchanger 6, and then returned to the compressor 3.
  • the air conditioner 101 of FIG. 1 controls each part so that the temperature detected by the temperature sensor 11 on the indoor side, that is, the room temperature becomes a target value. That is, the air conditioner 101 controls the rotation speed of the compressor 3, the opening degree of the expansion valve 7, the air volume blown by the indoor blower 8, and the air volume blown by the outdoor blower 9.
  • This control is performed based on the temperature detected by the temperature sensors 10 to 14, and the cooling capacity or the heating capacity of the air conditioner 101 is controlled. Such control is performed by the control unit 15 of the outdoor unit 2.
  • frost may form on the outdoor heat exchanger 6 that functions as an evaporator.
  • the ventilation resistance of the outdoor blower 9 increases, the amount of heat exchanged in the outdoor heat exchanger 6 decreases, and the heating capacity decreases. Therefore, the air conditioner 101 performs a defrosting operation to melt the frost of the outdoor heat exchanger 6.
  • FIG. 2 is a flowchart showing an example of defrosting operation control of the air conditioner 101 according to the first embodiment.
  • the defrosting operation of FIG. 2 is a general operation example, and is not limited to this.
  • the outline of the defrosting operation of FIG. 2 will be described.
  • the temperature of the refrigerant pipe of the outdoor heat exchanger 6 detected by the temperature sensor 14 is referred to as a defrosting temperature ⁇ . Therefore, the defrosting temperature ⁇ is the temperature of the outdoor heat exchanger 6 when the outdoor heat exchanger 6 functions as an evaporator during heating.
  • step S1 the control unit 15 determines whether a certain time has elapsed since the air conditioner 101 started or restarted the heating operation.
  • the fixed time is a preset value.
  • the control unit 15 ends the process of the flow of FIG. 2 as it is.
  • the control unit 15 proceeds to step S2.
  • step S2 the control unit 15 determines whether the defrosting temperature ⁇ detected by the temperature sensor 14 is equal to or less than a preset specified value. When the control unit 15 determines that the defrosting temperature ⁇ is larger than the specified value, the control unit 15 ends the flow processing of FIG. 2 as it is. On the other hand, when the control unit 15 determines that the defrosting temperature ⁇ is equal to or lower than the specified value, the control unit 15 proceeds to step S3.
  • step S3 the control unit 15 determines that the conditions for starting the defrosting operation are satisfied by the determination in steps S1 and S2, that is, that the outdoor heat exchanger 6 is frosted.
  • the control unit 15 temporarily stops the compressor 3 in order to start the defrosting operation.
  • step S4 the control unit 15 switches the four-way valve 4 to form the cooling operation circuit described above, restarts the compressor 3, and starts the defrosting operation.
  • the defrosting operation for example, the frost adhering to the outdoor heat exchanger 6 is melted by a reverse defrosting method in which a refrigerant is circulated.
  • the refrigerant that has become high temperature and high pressure in the compressor 3 flows through the refrigerant pipe to the outdoor heat exchanger 6.
  • the refrigerant gives heat to the frost adhering to the outdoor heat exchanger 6, so that the frost melts and becomes water.
  • step S5 the control unit 15 determines whether the defrosting temperature ⁇ is equal to or higher than the specified value.
  • step S5 when the control unit 15 determines that the defrosting temperature ⁇ is equal to or higher than the specified value, the control unit 15 proceeds to step S6.
  • step S6 the control unit 15 terminates the defrosting operation on the assumption that the condition for ending the defrosting operation is satisfied, and proceeds to step S7.
  • the control unit 15 At the end of the defrosting operation, the control unit 15 first stops the compressor 3 and switches the four-way valve 4 to return to the heating operation circuit.
  • step S7 the control unit 15 restarts the compressor 3 to restart the heating operation.
  • the inference value Pest of the "frost melting time” described later is obtained by using the trained model.
  • the control unit 15 of the air conditioner 101 determines whether or not to start the flow of FIG. 2 based on the inferred value Pest of the “time required for frost melting”. This makes it possible to avoid performing unnecessary defrosting operation when the outdoor heat exchanger 6 is not frosted. Hereinafter, it will be described in detail.
  • FIG. 3 is a diagram showing an example of a change over time in the defrosting temperature ⁇ during the defrosting operation.
  • the horizontal axis is time and the vertical axis is the defrosting temperature ⁇ .
  • the change pattern of the defrosting temperature ⁇ with time varies depending on the values of various parameters such as the outside air temperature, the amount of frost adhering to the outdoor heat exchanger 6, and the number of drives of the compressor 3 during the defrosting operation. Therefore, it is not limited to that shown in FIG.
  • the case shown in FIG. 3 will be described as an example.
  • the defrosting temperature ⁇ when the defrosting operation is started at time t0, the defrosting temperature ⁇ after the start of the defrosting operation increases. After that, the refrigerant flowing through the refrigerant pipe of the outdoor heat exchanger 6 gives heat to the frost adhering to the outdoor heat exchanger 6 to melt the frost. Therefore, the defrosting temperature ⁇ temporarily stabilizes at around 0 ° C., and rises again after the frost in the outdoor heat exchanger 6 melts. After that, as described above, the defrosting operation is terminated when the defrosting temperature ⁇ becomes equal to or higher than the specified value.
  • two temperatures ⁇ 1 and ⁇ 2 are set in the vicinity of 0 ° C.
  • the temperature ⁇ 1 is set to a temperature lower than the temperature ⁇ 2 ( ⁇ 1 ⁇ 2).
  • the temperatures ⁇ 1 and ⁇ 2 are set so that, for example, the relationship ⁇ 1 ⁇ 0 ⁇ 2 holds.
  • the temperature ⁇ 1 is a temperature immediately before the defrosting temperature ⁇ stabilizes.
  • the temperature ⁇ 2 is the temperature immediately before the defrosting temperature ⁇ rises again. Let t1 be the time when the defrosting temperature ⁇ becomes the temperature ⁇ 1, and let t2 be the time when the defrosting temperature ⁇ becomes the temperature ⁇ 2.
  • the period in which the time t satisfies the relationship of t1 ⁇ t ⁇ t2 is the period in which the defrosting temperature ⁇ exists in the vicinity of the melting point, and specifically, the period in which ⁇ 1 ⁇ ⁇ ⁇ ⁇ 2.
  • the period is a period in which the refrigerant flowing through the refrigerant pipe of the outdoor heat exchanger 6 gives heat to the frost adhering to the outdoor heat exchanger 6 to melt the frost.
  • the period is referred to as “time required for frost melting” and is indicated by the reference numeral “P” in FIG.
  • the frost melting time P is the period during which the defrosting temperature ⁇ , which is the defrosting temperature information, stabilizes within the first range.
  • the time required for frost melting P is a period during which a state change occurs in which the frost adhering to the outdoor heat exchanger 6 changes to water. Therefore, during the frost melting required time P, the thermal energy of the refrigerant flowing through the refrigerant pipe is consumed not as sensible heat in which the defrosting temperature ⁇ changes, but as latent heat in which a state change in which frost changes to water occurs.
  • the required frost melting time P is a period during which the frost adhering to the outdoor heat exchanger 6 is melted while the defrosting temperature ⁇ , which is the defrosting temperature information, changes so as to stick to the vicinity of the melting point. Therefore, in the defrosting time required P, the defrosting temperature ⁇ , which is the defrosting temperature information, is stable within the first range.
  • the temperature ⁇ 1 and the temperature ⁇ 2 are appropriately set so that, for example, the relationship of ⁇ 1 ⁇ 0 ⁇ 2 is established.
  • the melting point may fluctuate slightly from 0 ° C. due to the influence of impurities such as dirt, among ⁇ 1 ⁇ 0 ⁇ 2, ⁇ 1 ⁇ 2 ⁇ 0, or 0 ⁇ 1 ⁇ 2. It may be set so that any one of the relationships holds.
  • the temperatures ⁇ 1 and ⁇ 2 are set in the range of ⁇ 20 ° C. to + 20 ° C., preferably in the range of ⁇ 5 ° C. to + 5 ° C., or in the range of ⁇ 10 ° C. to + 10 ° C. In either case, the "first range" is a range including the melting point or 0 ° C.
  • the time required for frost melting P varies depending on the amount of frost adhering to the outdoor heat exchanger 6.
  • the amount of frost is large, it takes a long time to completely melt the frost, so that the time required for frost melting P becomes long.
  • the amount of frost is small, the time required for frost melting P becomes short.
  • the defrosting temperature ⁇ becomes temporarily stable at around 0 ° C. as shown in FIG. 3 after the temperature ⁇ 1 or higher. The temperature may reach ⁇ 2 or higher without doing so.
  • the defrosting temperature ⁇ may already be ⁇ 1 ⁇ ⁇ ⁇ 2 at time t0.
  • the defrosting temperature ⁇ temporarily changes from ⁇ ⁇ 1 to ⁇ 1 due to an unstable refrigerant state due to the stop and start of the compressor 3 during the defrosting operation and a series of transient phenomena thereof. After ⁇ ⁇ ⁇ 2, ⁇ ⁇ 1 may be set again.
  • the time when ⁇ 1 ⁇ ⁇ ⁇ 2 is set for the second time is set as the start point t1 of the count of the required frost melting time P.
  • the time when ⁇ 1 ⁇ ⁇ ⁇ 2 at the end is set as the start point t1 of the count.
  • the defrosting temperature ⁇ temporarily changes from the state of ⁇ 1 ⁇ ⁇ ⁇ 2 due to an unstable refrigerant state due to the stop and start of the compressor 3 during the defrosting operation and a series of transient phenomena thereof. In some cases, ⁇ 2 ⁇ ⁇ and then ⁇ 1 ⁇ ⁇ ⁇ 2 again.
  • the time when ⁇ 2 ⁇ ⁇ is satisfied for the second time is set as the end point t2 of the count of the required frost melting time P.
  • the time when ⁇ 2 ⁇ ⁇ at the end is set as the end point t2 of the count.
  • the method of determining the start point and the end point of the count of the required frost melting time P is an example, and is not limited to the above.
  • the control unit 15 transmits the control values such as the operating frequency of the compressor 3, the current value of the outdoor blower 9, the detection values of the temperature sensors 10 to 14, and the required time for frost melting to the control unit 15. Store with the provided storage device.
  • the inference device 20 according to the first embodiment obtains the inferred value Pest of the frost melting required time P shown in FIG. 3 above, and outputs the inferred value Pest of the frost melting required time P to the air conditioner 101. .. Then, the air conditioner 101 according to the first embodiment processes the flow of FIG. 2 only when the inferred value Pest of the frost melting required time P output from the inference device 20 is equal to or greater than a predetermined threshold value Th. To carry out. Therefore, in the first embodiment, the defrosting operation is performed when all of the following three conditions (a) to (c) are satisfied. This makes it possible to avoid unnecessary defrosting operation.
  • FIG. 4 is a block diagram showing a configuration of the inference device 20 according to the first embodiment.
  • the inference device 20 includes a first data acquisition unit 21 and an inference unit 22. Further, the learned model storage unit 33 or the external device 40 is connected to the inference device 20.
  • the inference device 20 may be provided in the air conditioner 101 as one component of the air conditioner 101 shown in FIG. In that case, the inference device 20 is built in, for example, the outdoor unit 2 of the air conditioner 101. Alternatively, the inference device 20 may be provided separately from the air conditioner 101. For example, the inference device 20 may exist on the cloud server. In that case, the control unit 15 of the air conditioner 101 and the inference device 20 are connected so as to be able to communicate with each other.
  • the first data acquisition unit 21 acquires the defrosting temperature ⁇ detected by the temperature sensor 14 as the defrosting temperature information. As described above, the defrosting temperature ⁇ is the temperature of the outdoor heat exchanger 6 detected by the temperature sensor 14. The first data acquisition unit 21 may acquire the defrosting temperature ⁇ directly from the temperature sensor 14, or may acquire the defrosting temperature ⁇ from the temperature sensor 14 via the control unit 15.
  • the inference unit 22 obtains the inferred value Pest of the time required for frost melting using the trained model. Specifically, the inference unit 22 inputs the defrosting temperature ⁇ acquired by the first data acquisition unit 21 into the trained model, and obtains the inferred value Pest of the frost melting required time P from the defrosting temperature ⁇ .
  • the trained model is generated by the learning device 30 described later and is described in the trained model storage unit 33.
  • the inference unit 22 acquires the trained model from the trained model storage unit 33.
  • the inference unit 22 acquires the trained model from the external device 40 via a communication line such as the Internet.
  • the external device 40 is, for example, one or more other air conditioners, a cloud server, a homepage of a manufacturer of the air conditioner 101, and the like.
  • the inference device 20 is composed of a processing circuit that realizes the functions of the first data acquisition unit 21 and the inference unit 22.
  • the processing circuit is composed of dedicated hardware or a processor.
  • the dedicated hardware is, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).
  • the processor executes a program stored in memory.
  • the inference device 20 has a storage unit (not shown) for storing the program, calculation results, and the like.
  • the storage unit is composed of a memory.
  • the memory is a non-volatile or volatile semiconductor memory such as RAM (RandomAccessMemory), ROM (ReadOnlyMemory), flash memory, EPROM (ErasableProgrammableROM), or a disk such as a magnetic disk, flexible disk, or optical disk.
  • RAM RandomAccessMemory
  • ROM ReadOnlyMemory
  • flash memory EPROM (ErasableProgrammableROM)
  • disk such as a magnetic disk, flexible disk, or optical disk.
  • FIG. 5 is a flowchart showing a processing flow of the inference device 20 according to the first embodiment.
  • the flow processing of FIG. 5 is repeated, for example, at regular intervals while the air conditioner 101 is performing the heating operation.
  • step S21 the first data acquisition unit 21 acquires the current defrosting temperature ⁇ detected by the temperature sensor 14.
  • the defrosting temperature ⁇ at this time is the temperature of the outdoor heat exchanger 6 when the outdoor heat exchanger 6 of the air conditioner 101 is functioning as an evaporator, and is the temperature before starting the defrosting operation. Is.
  • step S22 the inference unit 22 inputs the defrosting temperature ⁇ acquired in step S21 into the learned model stored in the learned model storage unit 33, and sets the inferred value Pest of the frost melting time P. obtain.
  • the trained model will be described later.
  • step S23 the inference unit 22 outputs the inference value Pest of the frost melting required time P obtained by the trained model to the control unit 15 of the air conditioner 101.
  • the control unit 15 of the air conditioner 101 receives the inferred value Pest of the frost melting time P from the inference device 20 and performs the process of FIG.
  • FIG. 6 is a flowchart showing a processing flow of the air conditioner 101 according to the first embodiment.
  • step S31 the control unit 15 of the air conditioner 101 determines whether the inferred value Pest of the frost melting required time P received from the inference device 20 is smaller than the preset threshold value Th. ..
  • the control unit 15 determines that the inferred value Pest of the frost melting time P is smaller than the threshold value Th.
  • step S32 the control unit 15 determines that the inferred value Pest of the frost melting time P is equal to or greater than the threshold value Th.
  • step S32 the control unit 15 determines that the defrosting operation is not performed, and ends the process of FIG. 6 as it is.
  • step S33 the control unit 15 proceeds to process the flow of FIG. In the flow of FIG. 2, when the condition of step S1 and the condition of step S2 are satisfied, the control unit 15 performs the defrosting operation.
  • the air conditioner 101 compares the inferred value Pest of the frost melting required time P output from the inference device 20 with the threshold value Th.
  • the inferred value Pest of the required frost melting time P is smaller than the threshold Th, a certain time has elapsed since the heating operation was started (S1 in FIG. 2 is YES), and the defrosting temperature ⁇ is equal to or less than the specified value. Even in the case of (S2 in FIG. 2 is YES), the defrosting operation is not performed. As a result, it is possible to avoid unnecessary defrosting operation, reduce power consumption, and prevent deterioration of user comfort due to defrosting operation.
  • the inference device 20 appropriately determines the start time of the defrosting operation by obtaining the inferred value Pest of the frost melting time P as the frost formation amount, and improves the energy saving. Can be planned.
  • the defrosting temperature information may be a value indicating a state of change in the temperature of the outdoor heat exchanger 6. That is, the defrosting temperature information is, for example, at least one of the average value, the cumulative value, the integrated value, the maximum value, and the minimum value of the defrosting temperature ⁇ in the time interval from the end of the previous defrosting operation to the present. It may be used as one. Further, the defrosting temperature information may be a gradient ⁇ of the defrosting temperature ⁇ with respect to the time t, as shown in FIG.
  • the gradient ⁇ indicates the ratio of the amount of change in the defrosting temperature ⁇ to the amount of change in time t. Incidentally, as shown in FIG. 3, the gradient ⁇ becomes 0 or almost 0 during the period of the time required for frost melting P.
  • the inference device 20 may use the humidity information in addition to the defrosting temperature information to obtain the inferred value Pest of the frost melting required time P.
  • the first data acquisition unit 21 acquires defrost temperature information from the temperature sensor 14, and further acquires the humidity of the outside air environment in which the outdoor unit 2 is installed as humidity information from the humidity sensor 17.
  • the trained model is a trained model for inferring the required time for frost melting from the defrost temperature information and the humidity information.
  • the inference unit 22 obtains the inference value Pest of the frost melting time P using the trained model.
  • the inference device 20 may infer the frost melting time P by using the current information of the outdoor blower 9 possessed by the outdoor unit 2 of the air conditioner 101 in addition to the defrost temperature information. ..
  • the amount of frost formed on the outdoor heat exchanger 6 increases, the ventilation property of the outdoor heat exchanger 6 deteriorates, the load on the outdoor blower 9 increases, and the current value increases. Therefore, it is highly possible that the frost on the outdoor heat exchanger 6 increases as the current value increases with respect to the rotation speed of the outdoor blower 9, and the time required for frost melting P tends to increase. ..
  • the first data acquisition unit 21 acquires the defrosting temperature information from the temperature sensor 14, and further acquires the current value used by the outdoor blower 9 for ventilation from the current measuring device 16 as the current information.
  • the trained model is a trained model for inferring the required time for frost melting from the defrost temperature information and the humidity information.
  • the inference unit 22 obtains the inference value Pest of the frost melting time P using the trained model.
  • the inference device 20 may infer the required time for frost melting P by using the outside air temperature information in addition to the defrost temperature information.
  • the first data acquisition unit 21 acquires the defrosting temperature information from the temperature sensor 14, and further acquires the outside air temperature from the temperature sensor 13 as the outside air temperature information.
  • the trained model is a trained model for inferring the required time for frost melting from the defrosting temperature information and the outside air temperature information.
  • the inference unit 22 obtains the inference value Pest of the frost melting time P using the trained model.
  • the inference device 20 may infer the frost melting required time P by using the illuminance information in addition to the defrost temperature information.
  • the first data acquisition unit 21 acquires the defrosting temperature information from the temperature sensor 14, and further acquires the illuminance of the outside air environment in which the outdoor unit 2 is installed from the illuminance sensor 18 as the illuminance information.
  • the trained model is a trained model for inferring the required time for frost melting from the defrost temperature information and the illuminance information.
  • the inference unit 22 obtains the inference value Pest of the frost melting time P using the trained model.
  • the inference device 20 infers the frost melting time P by the trained model using at least one of the defrost temperature information, the humidity information, the current information, the outside air temperature information, and the illuminance information.
  • the value Pest may be obtained.
  • the combination of these information can be changed as appropriate.
  • the inference device 20 may acquire these information directly from each sensor, but may also acquire the information via the control unit 15.
  • the inference device 20 outputs the inference value Pest of the frost melting time P using the learned model learned by the model generation unit 32 of the learning device 30.
  • the inference device 20 acquires a trained model from the external device 40 and outputs the inferred value Pest of the frost melting time P based on the trained model. You may.
  • FIG. 7 is a block diagram showing the configuration of the learning device 30 according to the first embodiment.
  • the learning device 30 includes a second data acquisition unit 31, a model generation unit 32, and a trained model storage unit 33.
  • the second data acquisition unit 31 acquires the defrosting temperature ⁇ as the defrosting temperature information and the actually measured value Pm of the required frost melting time P. Further, the second data acquisition unit 31 generates learning data by combining the defrosting temperature ⁇ and the actually measured value Pm of the required frost melting time P.
  • the defrosting temperature ⁇ is the surface temperature of the outdoor heat exchanger 6 included in the air conditioner 101, and is a value detected by the temperature sensor 14.
  • the measured value Pm of the required frost melting time P is a period during which the temperature of the outdoor heat exchanger 6 actually exists near the melting point during the defrosting operation of the air conditioner 101.
  • the actually measured value Pm of the required frost melting time P specifically shows that the defrosting temperature ⁇ detected by the temperature sensor 14 has a relationship of ⁇ 1 ⁇ ⁇ ⁇ ⁇ 2 with respect to the temperatures ⁇ 1 and the temperature ⁇ 2 shown in FIG. The length of time that was met.
  • the measured value Pm of the frost melting required time P is measured by the control unit 15 of the air conditioner 101. That is, the control unit 15 calculates the measured value Pm of the required frost melting time P based on the defrosting temperature ⁇ detected by the temperature sensor 14.
  • the second data acquisition unit 31 may acquire the defrosting temperature ⁇ and the measured value Pm of the required frost melting time P from the control unit 15, or the defrosting temperature ⁇ may be directly obtained from the temperature sensor 14. It may be obtained from.
  • the model generation unit 32 takes the frost melting time based on the learning data created based on the combination of the defrosting temperature ⁇ output from the second data acquisition unit 31 and the measured value Pm of the frost melting time P. Learn P. That is, the model generation unit 32 generates a trained model for inferring the optimum frost melting time P from the actual measurement value Pm of the defrosting temperature ⁇ and the frost melting time P of the air conditioner 101.
  • the learning data is data in which the defrosting temperature ⁇ and the actually measured value Pm of the required frost melting time P are associated with each other.
  • the defrosting temperature information may be a value indicating the current defrosting temperature ⁇ or a state of change in the defrosting temperature ⁇ . That is, the defrosting temperature information is the current defrosting temperature ⁇ , or the average value, cumulative value, integral value, maximum value, or maximum value of the defrosting temperature ⁇ in the time interval from the end of the previous defrosting operation to the present. It may be at least one of the minimum values.
  • the learning device 30 is used to learn the frost melting time P of the air conditioner 101.
  • the learning device 30 may be provided by the air conditioner 101 as one component, but may be provided separately from the air conditioner 101. In that case, the learning device 30 is connected to the air conditioning device 101 via a network, for example, together with the inference device 20. Further, both the learning device 30 and the inference device 20 may be built in the air conditioner 101. Further, the learning device 30 and the inference device 20 may exist on the cloud server.
  • the learning algorithm used for learning by the model generation unit 32 known algorithms such as supervised learning, unsupervised learning, and reinforcement learning can be used. As an example, a case where a neural network is applied will be described.
  • the model generation unit 32 learns the frost melting required time P by so-called supervised learning according to, for example, a neural network model.
  • supervised learning refers to a method of learning a feature in the learning data by giving a set of input and result (label) data to the learning device 30, and inferring the result from the input.
  • a neural network is composed of an input layer consisting of a plurality of neurons, an intermediate layer (hidden layer) consisting of a plurality of neurons, and an output layer consisting of a plurality of neurons.
  • the intermediate layer may be one layer or two or more layers.
  • FIG. 8 is a diagram schematically showing an example of a model of the neural network 34 included in the model generation unit 32.
  • the neural network 34 of the model generation unit 32 is, for example, a three-layer neural network as shown in FIG. 8 will be described as an example.
  • the neural network 34 has three input layers X1, X2, X3, two intermediate layers Y1, Y2, and three output layers Z1, Z2, Z3.
  • the first preset input In1, In2, and In3 are set.
  • the weight W1 is multiplied.
  • the first weight W1 of the input layer X1 has two types, w11 and w12.
  • the first weight W1 of the input layer X2 has two types of w13 and w14
  • the first weight W1 of the input layer X3 has two types of w15 and w16.
  • the multiplication result obtained by multiplying the first weight W1 is input to the intermediate layers Y1 and Y2.
  • the multiplication result is referred to as a first multiplication result.
  • the preset second weight W2 is multiplied by the first multiplication result.
  • the second weight W2 of the intermediate layer Y1 has three types, w21, w23, and w25.
  • the second weight W2 of the intermediate layer Y2 has three types, w22, w24, and w26.
  • the multiplication result obtained by multiplying the second weight W2 is input to the output layers Z1, Z2, and Z3.
  • the multiplication result is referred to as a second multiplication result.
  • the second multiplication result is output from the output layers Z1, Z2, and Z3 as output results Out1, Out2, and Out3.
  • the output results Out1, Out2, and Out3 change depending on the values of the weights W1 and W2.
  • the neural network 34 is subjected to so-called supervised learning according to the learning data created based on the combination of the defrosting temperature ⁇ and the required frost melting time Pm acquired by the second data acquisition unit 31. Learn the time required for frost melting P.
  • the neural network 34 learns the frost melting time P for the defrosting temperature ⁇ by the following procedure.
  • the output results Out1, Out2, and Out3 output from the output layers Z1, Z2, and Z3 when the defrosting temperature ⁇ is input to the input layers X1, X2, and X3 approach the measured value Pm of the frost melting time P.
  • the first weight W1 and the second weight W2 are adjusted.
  • the first weight W1 and the second weight W2 are set, and the required frost melting time P with respect to the defrosting temperature ⁇ is learned.
  • the model generation unit 32 generates and outputs a trained model of the frost melting time P for the defrosting temperature ⁇ by executing the above learning.
  • the trained model storage unit 33 stores the trained model output from the model generation unit 32.
  • the learning device 30 is composed of a processing circuit that realizes the functions of the second data acquisition unit 31 and the model generation unit 32.
  • the processing circuit is composed of dedicated hardware or a processor.
  • the dedicated hardware is, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).
  • the processor executes a program stored in memory.
  • the learning device 30 has a storage device (not shown) for storing the program, calculation results, and the like.
  • the storage device realizes the function of the trained model storage unit 33.
  • the storage device is composed of a memory.
  • the memory is a non-volatile or volatile semiconductor memory such as RAM (RandomAccessMemory), ROM (ReadOnlyMemory), flash memory, EPROM (ErasableProgrammableROM), or a disk such as a magnetic disk, flexible disk, or optical disk.
  • RAM RandomAccessMemory
  • ROM ReadOnlyMemory
  • flash memory EPROM (ErasableProgrammableROM)
  • disk such as a magnetic disk, flexible disk, or optical disk.
  • FIG. 9 is a flowchart showing a processing flow of the learning device 30 according to the first embodiment.
  • the second data acquisition unit 31 acquires the defrosting temperature ⁇ from the temperature sensor 14, and acquires the actually measured value Pm of the frost melting time P from the control unit 15.
  • the actual measurement value Pm of the defrosting temperature ⁇ and the required frost melting time P is acquired at the same time, but the present invention is not limited to this case. Since it is sufficient that the second data acquisition unit 31 can acquire the defrosting temperature ⁇ and the measured value Pm of the frost melting time P in association with each other, the second data acquisition unit 31 can acquire the data of the defrosting temperature ⁇ and the frost melting time.
  • the data of the measured value Pm of P may be acquired at different timings.
  • step S42 the model generation unit 32 performs a learning process using the learning data.
  • the training data includes the defrosting temperature ⁇ and the measured value Pm of the required frost melting time P. More specifically, the learning data is the learning data created based on the combination of the defrosting temperature ⁇ acquired by the second data acquisition unit 31 and the actually measured value Pm of the required frost melting time P. Is.
  • the model generation unit 32 learns the frost melting required time P with respect to the defrosting temperature ⁇ by so-called supervised learning using the learning data, and generates a trained model.
  • step S43 the trained model storage unit 33 stores the trained model generated by the model generation unit 32.
  • the present invention is not limited to this.
  • the learning algorithm used by the model generation unit 32 it is also possible to apply reinforcement learning, unsupervised learning, semi-supervised learning, or the like, in addition to supervised learning.
  • the model generation unit 32 determines the time required for frost melting according to the learning data created for the plurality of air conditioners 101. You may try to learn P. That is, the second data acquisition unit 31 may acquire learning data from a plurality of air conditioners 101 used in the same area in order for the model generation unit 32 to learn the frost melting required time P. Alternatively, the second data acquisition unit 31 may acquire learning data collected from a plurality of air conditioners 101 that operate independently in different areas. Further, the second data acquisition unit 31 can add the air conditioner 101 for collecting learning data to the list on the way, or, conversely, exclude it from the list on the way. Further, the learning device 30 that has learned the frost melting time P for a certain air conditioner 101 is applied to another air conditioner 101, and the frost melting time P is repeated for the other air conditioner 101. You may learn and update.
  • model generation unit 32 deep learning for learning the extraction of the feature amount itself can also be used, and other known methods such as genetic programming, functional logic programming, and support can be used.
  • Machine learning may be performed according to a vector machine or the like.
  • the inference device 20 includes a first data acquisition unit 21 for acquiring defrost temperature information of the air conditioner 101. Further, the inference unit 22 of the inference device 20 uses the trained model to obtain the inferred value Pest of the frost melting time P for the defrost temperature information based on the defrost temperature information acquired by the first data acquisition unit 21. Ask. The air conditioner 101 compares the inferred value Pest of the frost melting required time P obtained by the inference device 20 with the threshold value Th.
  • the air conditioner 101 does not start the defrosting operation even if a certain time has elapsed from the start of the heating operation and the defrosting temperature ⁇ is equal to or less than the specified value. As a result, it is possible to suppress unnecessary defrosting operation. As a result, the power consumption of the air conditioner 101 is reduced, and it is possible to prevent the user's comfort from being lowered due to unnecessary defrosting operation.
  • the defrosting operation is performed.
  • defrosting can be performed at an appropriate timing when defrosting operation is required.
  • the learning device 30 includes a second data acquisition unit 31 that acquires learning data created based on a combination of the defrosting temperature information and the actually measured value Pm of the frost melting time P. ing. Further, the model generation unit 32 of the learning device 30 learns using the learning data so that the inferred value Pest of the frost melting required time P for the defrosting temperature information approaches the measured value Pm of the frost melting required time P. .. As a result, the model generation unit 32 generates a trained model for obtaining the inferred value Pest of the frost melting time P from the defrosting temperature information of the air conditioner 101. Since the learning device 30 generates a trained model using the learning data including the measured value Pm of the frost melting time P, it is possible to accurately obtain the inferred value Pest close to the measured value Pm of the frost melting time P. can.
  • the start time of the defrosting operation is appropriately determined by obtaining the inferred value Pest of the required frost melting time P as the amount of frost formation. , It is possible to improve energy saving.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Air Conditioning Control Device (AREA)
PCT/JP2020/034709 2020-09-14 2020-09-14 推論装置および学習装置 WO2022054278A1 (ja)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CN202080104245.2A CN115997089A (zh) 2020-09-14 2020-09-14 推断装置以及学习装置
DE112020007604.0T DE112020007604T5 (de) 2020-09-14 2020-09-14 Folgerungsgerät und Lerngerät
PCT/JP2020/034709 WO2022054278A1 (ja) 2020-09-14 2020-09-14 推論装置および学習装置
JP2022547361A JPWO2022054278A5 (ja) 2020-09-14 推論装置、学習装置、および、空気調和装置

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2020/034709 WO2022054278A1 (ja) 2020-09-14 2020-09-14 推論装置および学習装置

Publications (1)

Publication Number Publication Date
WO2022054278A1 true WO2022054278A1 (ja) 2022-03-17

Family

ID=80631459

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/034709 WO2022054278A1 (ja) 2020-09-14 2020-09-14 推論装置および学習装置

Country Status (3)

Country Link
CN (1) CN115997089A (de)
DE (1) DE112020007604T5 (de)
WO (1) WO2022054278A1 (de)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7339570B1 (ja) 2022-05-20 2023-09-06 ダイキン工業株式会社 予測装置、冷凍システム、予測方法及び予測プログラム

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0783484A (ja) * 1993-09-09 1995-03-28 Toshiba Corp 空気調和機
JP2011252681A (ja) * 2010-06-04 2011-12-15 Hitachi Appliances Inc 冷蔵庫
CN104933322A (zh) * 2015-07-11 2015-09-23 湖南大学 一种前置式防、融霜方法
JP2015183873A (ja) * 2014-03-20 2015-10-22 日立アプライアンス株式会社 空気調和機
JP2017040400A (ja) * 2015-08-18 2017-02-23 ダイキン工業株式会社 空気調和機

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0783484A (ja) * 1993-09-09 1995-03-28 Toshiba Corp 空気調和機
JP2011252681A (ja) * 2010-06-04 2011-12-15 Hitachi Appliances Inc 冷蔵庫
JP2015183873A (ja) * 2014-03-20 2015-10-22 日立アプライアンス株式会社 空気調和機
CN104933322A (zh) * 2015-07-11 2015-09-23 湖南大学 一种前置式防、融霜方法
JP2017040400A (ja) * 2015-08-18 2017-02-23 ダイキン工業株式会社 空気調和機

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7339570B1 (ja) 2022-05-20 2023-09-06 ダイキン工業株式会社 予測装置、冷凍システム、予測方法及び予測プログラム
WO2023223594A1 (ja) * 2022-05-20 2023-11-23 ダイキン工業株式会社 予測装置、冷凍システム、予測方法及び予測プログラム
JP2023170830A (ja) * 2022-05-20 2023-12-01 ダイキン工業株式会社 予測装置、冷凍システム、予測方法及び予測プログラム

Also Published As

Publication number Publication date
JPWO2022054278A1 (de) 2022-03-17
CN115997089A (zh) 2023-04-21
DE112020007604T5 (de) 2023-06-22

Similar Documents

Publication Publication Date Title
JP6642379B2 (ja) 空調機
CN111981640B (zh) 一种除霜控制方法、装置、空调器及存储介质
JP2007225158A (ja) 除霜運転制御装置および除霜運転制御方法
CN111023450B (zh) 一种节能化霜的控制方法、控制装置、存储介质及空调
TW201741609A (zh) 冰箱
US10214183B2 (en) Method of defogging inner surface of vehicle windshield glass
CN110749040B (zh) 运行状态的检测方法、装置、风机、空调器和存储介质
JP2019020034A (ja) 空気調和機
CN112032941A (zh) 空调器的控制方法
WO2022054278A1 (ja) 推論装置および学習装置
JPH1151500A (ja) ヒートポンプ式空気調和機
JP7328498B2 (ja) 情報処理装置、空気調和装置、情報処理方法、空気調和方法、及びプログラム
JP2006145204A (ja) 空気調和機
CN113739460B (zh) 蒸发器除霜处理方法、装置及热泵设备
WO2014103620A1 (ja) 冷凍装置
JP4592599B2 (ja) 空気調和機
JP6408792B2 (ja) 空気調和機、空気調和機の制御方法、および空気調和機のための制御プログラム
JPH1194327A (ja) 空気調和機の制御装置
JP5212330B2 (ja) 空気調和機
JP2016080201A (ja) 電子制御装置
JP7093031B2 (ja) 情報処理装置、情報処理方法、及びプログラム
JP2016065699A (ja) 冷凍サイクル装置
WO2018029872A1 (ja) 空気調和機
JP7026844B2 (ja) 情報処理装置、空気調和装置および空気調和システム
CN114791154A (zh) 多联机空调及其清洁方法、控制装置和可读存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20953343

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2022547361

Country of ref document: JP

Kind code of ref document: A

WWE Wipo information: entry into national phase

Ref document number: 18004491

Country of ref document: US

122 Ep: pct application non-entry in european phase

Ref document number: 20953343

Country of ref document: EP

Kind code of ref document: A1